Development of a novel copper metabolism-related risk model to predict prognosis and tumor microenvironment of patients with stomach adenocarcinoma

Background: Stomach adenocarcinoma (STAD) is the fourth highest cause of cancer mortality worldwide. Alterations in copper metabolism are closely linked to cancer genesis and progression. We aim to identify the prognostic value of copper metabolism-related genes (CMRGs) in STAD and the characteristic of the tumor immune microenvironment (TIME) of the CMRG risk model.Methods: CMRGs were investigated in the STAD cohort from The Cancer Genome Atlas (TCGA) database. Then, the hub CMRGs were screened out with LASSO Cox regression, followed by the establishment of a risk model and validated by GSE84437 from the Expression Omnibus (GEO) database. The hub CMRGs were then utilized to create a nomogram. TMB (tumor mutation burden) and immune cell infiltration were investigated. To validate CMRGs in immunotherapy response prediction, immunophenoscore (IPS) and IMvigor210 cohort were employed. Finally, data from single-cell RNA sequencing (scRNA-seq) was utilized to depict the properties of the hub CMRGs.Results: There were 75 differentially expressed CMRGs identified, 6 of which were linked with OS. 5 hub CMRGs were selected by LASSO regression, followed by construction of the CMRG risk model. High-risk patients had a shorter life expectancy than those low-risk. The risk score independently predicted STAD survival through univariate and multivariate Cox regression analyses, with ROC calculation generating the highest results. This risk model was linked to immunocyte infiltration and showed a good prediction performance for STAD patients’ survival. Furthermore, the high-risk group had lower TMB and somatic mutation counters and higher TIDE scores, but the low-risk group had greater IPS-PD-1 and IPS-CTLA4 immunotherapy prediction, indicating a higher immune checkpoint inhibitors (ICIs) response, which was corroborated by the IMvigor210 cohort. Furthermore, those with low and high risk showed differential susceptibility to anticancer drugs. Based on CMRGs, two subclusters were identified. Cluster 2 patients had superior clinical results. Finally, the copper metabolism-related TIME of STAD was concentrated in endothelium, fibroblasts, and macrophages.Conclusion: CMRG is a promising biomarker of prognosis for patients with STAD and can be used as a guide for immunotherapy

Cyclic Load Responses of GFRP-Strengthened Hollow Rectangular Bridge Piers

This study investigated the seismic behavior of glass fiber reinforced polymer (GFRP) strengthened hollow rectangular bridge piers. Cyclic testing of reinforced concrete (RC) piers retrofitted with GFRP was carried out under constant axial loading and lateral bending. The failure characteristics, flexural ductility, dissipated energy, and hysteretic behaviors, were analyzed based on experimental results. A simplified GFRP-confined concrete model is developed by considering effective strength coefficient and area distribution ratio of GFRP sheets. The results indicate that the failure modes and damage region would be changed and the ductility and dissipated energy of the GFRP-strengthened hollow rectangular bridge piers were improved greatly but not much improvement for the lateral load capacity. The analytical results of the force-displacement hysteretic loops based on the GFRP-confined concrete model developed in this paper agreed well with the experimental data

Feature Interaction Aware Automated Data Representation Transformation

Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively. Recent advancements in automated feature engineering (AutoFE) have made significant progress in addressing various challenges associated with representation learning, issues such as heavy reliance on intensive labor and empirical experiences, lack of explainable explicitness, and inflexible feature space reconstruction embedded into downstream tasks. However, these approaches are constrained by: 1) generation of potentially unintelligible and illogical reconstructed feature spaces, stemming from the neglect of expert-level cognitive processes; 2) lack of systematic exploration, which subsequently results in slower model convergence for identification of optimal feature space. To address these, we introduce an interaction-aware reinforced generation perspective. We redefine feature space reconstruction as a nested process of creating meaningful features and controlling feature set size through selection. We develop a hierarchical reinforcement learning structure with cascading Markov Decision Processes to automate feature and operation selection, as well as feature crossing. By incorporating statistical measures, we reward agents based on the interaction strength between selected features, resulting in intelligent and efficient exploration of the feature space that emulates human decision-making. Extensive experiments are conducted to validate our proposed approach.Comment: Accepted to SIAM Conference on Data Mining(SDM) 202

BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer

BatGPT is a large-scale language model designed and trained jointly by Wuhan University and Shanghai Jiao Tong University. It is capable of generating highly natural and fluent text in response to various types of input, including text prompts, images, and audio. In the modeling level, we employ a bidirectional autoregressive architecture that allows the model to efficiently capture the complex dependencies of natural language, making it highly effective in tasks such as language generation, dialog systems, and question answering. Moreover, the bidirectional autoregressive modeling not only operates from left to right but also from right to left, effectively reducing fixed memory effects and alleviating model hallucinations. In the training aspect, we propose a novel parameter expansion method for leveraging the pre-training of smaller models and employ reinforcement learning from both AI and human feedback, aimed at improving the model's alignment performance. Overall, these approaches significantly improve the effectiveness of BatGPT, and the model can be utilized for a wide range of natural language applications

Food-Derived α-Glucosidase Inhibitory Peptides: Research Progress on Structure-Activity Relationship, Safety and Bioavailability

Type II diabetes mellitus (T2DM) is one of the most prevalent metabolic diseases worldwide and α-glucosidase inhibitors are oral medications that are effective in the prevention and management of T2DM. Food-derived bioactive peptides are potential sources of α-glucosidase inhibitors, which have shown to have hypoglycemic activity and can effectively control postprandial blood glucose by inhibiting α-glucosidase activity, thus intervening and regulating T2DM, and have great prospects in the development of hypoglycemic peptide products. At present, there is no systematic strategy for exploring the structure-activity relationship, safety and bioavailability of food-derived α-glucosidase inhibitory peptides, hampering their research and development. In this paper, we systematically review the structural characteristics, toxicity, allergenicity, and bioavailability (gastrointestinal digestive enzyme resistance, mucus permeability, transit efficiency in small intestinal epithelial cells and liver metabolism) of food-derived α-glucosidase inhibitory peptides that have been successfully identified in recent years. This review is expected to provide a theoretical reference for the rational development of food-derived α-glucosidase inhibitory peptides and the further processing of related functional foods

Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations

Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial correlations and temporal dependencies of traffic speed evolving patterns by leveraging explicit spatial relations (geographical proximity) through pre-defined geographical structures ({\it e.g.}, region grids or road networks). While achieving promising results, current traffic speed prediction methods still suffer from ignoring implicit spatial correlations (interactions), which cannot be captured by grid/graph convolutions. To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations. Specifically, we first develop a Dual-Transformer architecture, including a Spatial Transformer and a Temporal Transformer. The Spatial Transformer automatically learns the implicit spatial correlations across the road segments beyond the boundary of geographical structures, while the Temporal Transformer aims to capture the dynamic changing patterns of the implicit spatial correlations. Then, to further integrate both explicit and implicit spatial correlations, we propose a distillation-style learning framework, in which the existing traffic speed prediction methods are considered as the teacher model, and the proposed Dual-Transformer architectures are considered as the student model. The extensive experiments over three real-world datasets indicate significant improvements of our proposed framework over the existing methods

Research Progress in Detection and Elimination of Anti-nutritional Factors in Pulses

In addition to containing rich nutrients and biologically active substances, the pulses contain phytic acid, saponins, trypsin inhibitors, lectins and other anti-nutritional factors, which have multiple types and entail complex detection processes, with different detection capabilities required. In certain treatment methods, their structures can be destroyed, and their content can be lowered, furthermore, the nutrients in pulses can be preserved to the maximum extent during processing. This paper evaluates the detection methods of anti-nutritional factors in detail, analyzes and compares the advantages, disadvantages, applicability and sensitivity of different detection methods, and introduces the principles, processes, inhibition effects, advantages and disadvantages of several common anti-nutritional factor elimination methods, focuses mainly on heat treatment. This study aims to provide some references for the analytical method selection, improvement and control of anti-nutritional factors in pulses